A typical work machine, such as, for example, a tractor, dozer, loader, earth mover, or other such piece of equipment, may have any number of mechanical components and systems that are subject to fatigue damage which could lead to structural failures. One method for monitoring fatigue damage on a work machine structure is to perform a manual, visual inspection. However, such a method may be impractical for several reasons. First, such an inspection may not be as comprehensive as desired. This may be due, in part, to the difficulty in accessing some components of the work machine, such as when the structure in question is concealed and cannot be viewed without dismantling a portion of the work machine. Second, a manual inspection of structural systems can only be performed on a periodic basis, yet damage and resulting catastrophic failure still can occur between inspections. Third, a manual inspection may not be able to detect how much fatigue damage may have already occurred in the work machine, or predict the mean time till failure of one or more machine components based on the fatigue damage. While manual inspection may provide some insight into damage that is visible to an inspector, (e.g., large visible cracks in a machine component), internal damage may not be readily apparent through manual inspection (e.g., small internal cracks in a component).
Some systems have been proposed utilizing various ways of monitoring structures electronically to detect fatigue damage. However, these proposed systems have not adequately addressed the monitoring of structures with rapidly changing load pictures, such as movable work machines. This is due in part to the way these proposed systems collect data about the structure. These proposed systems may collect data about the structure at a relatively low sampling rate to ease the computing burden of performing analysis on the data and storing the analysis results. However, a low sampling rate may entirely miss some load states which endure very briefly.
Many critical load states experienced by a work machine may only endure very briefly. For example, when a wheel loader is digging and the bucket hits a rock, the load state may peak for a few brief moments before the rock is broken or dug out. In structures with rapidly changing load states, the sampling rate must be high in order to capture these peak load states which may endure only very briefly. If the sampling rate is too slow to “see” all or most of these critical load states, the analysis results will not accurately reflect the true condition of the structure.
However, for a complex structure rapid sampling rates may present an enormous challenge as the computing power required to analyze the rapidly sampled data in the traditional manner could be unachievable.
Another proposed system for monitoring the structural integrity of a structure is disclosed in U.S. Pat. No. 5,774,376 to Manning. The '376 patent discloses a system for monitoring the structural integrity of a mechanical structure utilizing a neural network to analyze data and characterize the structure's health. In use, a sensor attached to the mechanical structure senses vibrations and generates an output signal based on the vibrations. The sensor output signal is sent through control electronics to a neural network that generates an output that characterizes the structural integrity of the mechanical structure. However, the system disclosed in the '376 patent is subject to a number of shortcomings. Experimental results in the literature have suggested that changes in vibration signals that result from the presence of cracks are small unless the crack has already grown to a considerable size. The use of vibrations as an input also suggests that the structure must be excited with frequency content that at least partially activates one of the natural modes of the structure. Many structures never receive such input during normal operation, which would require that the excitation be delivered in some artificial manner, which could be cumbersome or impossible. Furthermore, the '376 patent provides a means of damage detection only. It does not provide any information on the usage habits or loading that would have been the underlying cause of that damage.